Data Science Roles Decoded
From Machine Learning Engineers to Analytics Consultants, discover the different roles of the data science industry, and learn the skills that distinguish you in each.
👋 Hey! This is Manisha Arora from PrepVector. Welcome to the Tech Growth Series, a newsletter that aims to bridge the gap between academic knowledge and practical aspects of data science. My goal is to simplify complicated data concepts, share my perspectives on the latest trends, and share my learnings from building and leading data teams.
This is Part 1 of a multi-part series about breaking data science roles and understanding how you can choose a role that best fits YOU. Enjoy the read!
Overview:
The data market is maturing and data science is now a key role in every organization, often embedding within the larger product org. There are multiple data roles within the industry, ranging from business analyst to machine learning engineer, and more. This causes a lot of confusion among aspiring (and even experienced) data professionals as they try to build their long-term career in this domain.
This edition of 'Data Science Roles Decoded' aims to demystify these roles, providing a clear, detailed breakdown of each position. We’ll explore what makes each role distinct and how to determine which might be the best fit for your skills and career aspirations.
It’s important to recognize that organizational structures vary significantly across different companies. The structuring of data teams—or any department for that matter—is influenced by the size and scope of the company. Larger organizations typically have the budget and infrastructure to support highly specialized data roles, while startups or smaller companies might benefit more from versatile data scientists who can wear multiple hats.
This newsletter focuses primarily on large-scale entities with the capacity to support dedicated, specialized data roles. Let us gain an understanding of these environments and where you might excel within the landscape of big organization.
The Two Ends of Data Roles:
There is a wide spectrum of data roles across most large organizations, with some overlap between different roles. You can visualize data roles on a spectrum - with one end focused on Engineering-heavy data role and the other end focused on business-heavy data role.
Engineering-focused roles are centered on the technical aspects of data management and analysis. Professionals in roles such as Data Engineers or Data Architects are crucial in designing, building, and maintaining the infrastructure that supports vast data volumes. Their responsibilities include working with databases, data warehouses, and managing ETL (Extract, Transform, Load) processes. The goal in these roles is to ensure that data pipelines are efficient, scalable, and secure, optimizing data workflows and maintaining the integrity and reliability of data systems.
Business-focused roles, including positions such as Data Analysts, Analytics Consultants, or Product Managers, prioritize using data to drive strategic decision-making and improve business outcomes. These professionals analyze data to uncover trends, patterns, and opportunities that can propel organizational growth and innovation. Their work often involves extracting actionable insights from data, which are used to inform product development, marketing strategies, and business operations. Additionally, they frequently collaborate with cross-functional teams to translate these insights into actionable recommendations and strategic initiatives.
You can visualize data roles on a spectrum - with one end focused on Engineering-heavy data role and the other end focused on business-heavy data role.
Breaking Down Data Roles:
Now let us break down each of the data role and understand what their key responsibilities are. Each of these roles plays a vital part in the broader ecosystem of data science, offering unique challenges and opportunities for professionals in the domain.
Applied Data Scientist:
Tackling real-world problems through the application of machine learning and statistical methods, these professionals implement scalable models into production, requiring a robust mix of software development skills and advanced analytics.
Research Data Scientist:
Positioned at the cutting edge of data science, they develop innovative algorithms and statistical techniques. Their work, which is crucial for advancing the field, often requires publication of findings and deep theoretical knowledge.
Machine Learning Engineer:
Specializing in deploying machine learning models at scale, these engineers integrate complex systems into existing data infrastructures, requiring extensive knowledge of cloud technologies and system architecture.
Marketing Data Scientist:
Focused on optimizing marketing strategies through data-driven insights, they employ advanced predictive analytics to enhance understanding of consumer behavior and drive marketing ROI.
Product Data Scientist / Product Analyst:
These professionals use data to inform product strategies, blending technical prowess with business insights to enhance product development and user engagement.
Data Analyst:
Experts in turning raw data into actionable insights, they utilize advanced statistical techniques and visualization tools to influence decision-making processes across organizations.
Data Product Manager / Consultant:
Managing the intersection of data and business strategy, they ensure that data projects align with organizational goals, translating complex data insights into actionable business initiatives.
Analytics Consultant:
These consultants help organizations optimize their data usage, advising on best practices in data management, analytics frameworks, and the ethical use of data to improve business outcomes.
AI Consultant:
Guiding firms through AI integration, these consultants tailor artificial intelligence solutions to specific industry needs, ensuring that companies leverage AI technologies for maximum impact.
Skills for Each Role:
I like to break down skills by:
Common skills across the board, needed for every data role
Specialized skills needed for specific DS roles
I like to segment data science skills as: a) Generic skills for every DS role, b) Specialized skills specific to each DS role
Data Science is a technical role, often under the engineer org of the organization. So it requires a variety of technical skills, irrespective of which specific data role you are in. To be successful in the role, it also requires interpersonal skills as you would have to often collaborate with multiple stakeholders across the org.
Technical Skills:
SQL (Data Pull): Essential for all roles, SQL skills allow you to retrieve and manipulate data from databases efficiently.
Python (Data Manipulation): Crucial across data science roles for scripting and automating data manipulation tasks.
Machine Learning (Algorithms and Implementation): Fundamental for roles like ML Engineers and Applied Scientists, focusing on developing and implementing predictive models.
Interpersonal Skills:
Scope Projects & Get Buy-in: Important for all roles to ensure projects are effectively scoped and supported by stakeholders.
Collaboration & Stakeholder Management: Critical for navigating complex team dynamics and ensuring cross-functional alignment.
Storytelling & Presenting Actionable Insights: Vital for roles that need to communicate complex data insights in a clear and impactful manner to drive decision-making.
All the technical and interpersonal skills above are relevant to every data science role. In addition, there are a few specialized skills you would need to learn, depending on whether you are (or would like to be) in a more engineering-heavy or business-heavy data role.
Role-Specific Skills:
These are skills specific to the role that need to be built on top of the generic/common skills. These are deep technical skills required to solve challenging problems, drive impact, and build a successful career in data science.
Engineering-focused Roles (e.g., ML Engineers, Applied Data Scientists):
Advanced ML: Beyond basic machine learning, these roles require a deep understanding of advanced algorithms and their real-world applications.
ML System Design: Involves designing systems that can efficiently implement ML algorithms at scale.
MLOps: Focuses on the operational aspects of machine learning, ensuring models are scalable, maintainable, and deployable.
Software Engineering Principles: Necessary for integrating ML models with existing software systems and ensuring robust, scalable implementations.
Business-focused Roles (e.g., Product & Marketing Data Scientists, Analytics Consultants):
Product Sense: Understanding of how products are developed and how data can enhance product features and user experience.
Problem Solving: Ability to tackle complex business problems using data-driven approaches.
A/B Testing: Skills in designing and interpreting A/B tests to drive product improvements.
Statistics: Strong statistical background to analyze data and derive meaningful insights.
These skills can be mastered depending upon which role you would like to pursue to build a long-term career.
If you liked this newsletter, check out my upcoming courses:
Master Product Sense and AB Testing, and learn to use statistical methods to drive product growth. I focus on inculcating a problem-solving mindset, and application of data-driven strategies, including A/B Testing, ML and Causal Inference, to drive product growth.
AI/ML Projects for Data Professionals
Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope ML Projects, get stakeholder buy-in, and execute the workflow from data exploration to model deployment. You will learn to use coding best practices to solve end-to-end AI/ML Projects to showcase to the employer or clients.
Upcoming Blog:
Assessing Your Strengths and Growth Areas as a DS to find a role for Long-Term Success.
Stay Tuned!